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Sensitivity analysis of a zeolite energy storage model: Impact of parameters on heat storage density and discharge power density

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  • Kuznik, Frédéric
  • Gondre, Damien
  • Johannes, Kévyn
  • Obrecht, Christian
  • David, Damien

Abstract

Physisorption heat storage in buildings can be a key technology for a more effective use of heating energy. However, a better understanding of key factors influencing the design and control of such systems is necessary. This paper presents the sensitivity analysis of the modeling parameters in the case of an open zeolite 13X/moist air heat storage system for building applications. The quantities of interest are the heat storage density and the discharge power density of the system. At the beginning, the whole analysis space is composed of 21 physical properties and 7 operating conditions and geometrical properties. After a first threshold selection, analysis of variance is carried on the remaining parameters, with a full factorial design of experiments to perform a complete sensitivity analysis of the model. The results show that only 3 thermophysical properties, i.e. the heat of adsorption, the water vapor molar mass and the adsorption equilibrium, and 3 operating conditions and system geometry parameters, i.e. the inlet relative humidity, the bed length and the inlet fluid flow rate, drive the outlet power density and heat storage density. The way those 6 parameters influence the outputs is also discussed and quantitatively assessed.

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  • Kuznik, Frédéric & Gondre, Damien & Johannes, Kévyn & Obrecht, Christian & David, Damien, 2020. "Sensitivity analysis of a zeolite energy storage model: Impact of parameters on heat storage density and discharge power density," Renewable Energy, Elsevier, vol. 149(C), pages 468-478.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:468-478
    DOI: 10.1016/j.renene.2019.12.035
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    References listed on IDEAS

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    1. Belen Zalba & Belen Sanchez-valverde & Jose Marin, 2005. "An experimental study of thermal energy storage with phase change materials by design of experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(4), pages 321-332.
    2. Henninger, Stefan K. & Ernst, Sebastian-Johannes & Gordeeva, Larisa & Bendix, Phillip & Fröhlich, Dominik & Grekova, Alexandra D. & Bonaccorsi, Lucio & Aristov, Yuri & Jaenchen, Jochen, 2017. "New materials for adsorption heat transformation and storage," Renewable Energy, Elsevier, vol. 110(C), pages 59-68.
    3. Bonanos, A.M. & Votyakov, E.V., 2016. "Sensitivity analysis for thermocline thermal storage tank design," Renewable Energy, Elsevier, vol. 99(C), pages 764-771.
    4. Kuznik, Frédéric & Gondre, Damien & Johannes, Kévyn & Obrecht, Christian & David, Damien, 2019. "Numerical modelling and investigations on a full-scale zeolite 13X open heat storage for buildings," Renewable Energy, Elsevier, vol. 132(C), pages 761-772.
    5. Kuznik, Frédéric & Johannes, Kevyn & Obrecht, Christian & David, Damien, 2018. "A review on recent developments in physisorption thermal energy storage for building applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 576-586.
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    Cited by:

    1. Gao, Shichao & Wang, Shugang & Sun, Yi & Wang, Jihong & Hu, Peiyu & Shang, Jiaxu & Ma, Zhenjun & Liang, Yuntao, 2023. "Effect of charging operating conditions on open zeolite/water vapor sorption thermal energy storage system," Renewable Energy, Elsevier, vol. 215(C).
    2. Strong, Curtis & Carrier, Ye & Handan Tezel, F., 2022. "Experimental optimization of operating conditions for an open bulk-scale silica gel/water vapour adsorption energy storage system," Applied Energy, Elsevier, vol. 312(C).
    3. Carla Delmarre & Marie-Anne Resmond & Frédéric Kuznik & Christian Obrecht & Bao Chen & Kévyn Johannes, 2021. "Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors," Energies, MDPI, vol. 14(11), pages 1-12, June.
    4. Zhang, Heng & Liu, Shuli & Shukla, Ashish & Zou, Yuliang & Han, Xiaojing & Shen, Yongliang & Yang, Liu & Zhang, Pengwei & Kusakana, Kanzumba, 2022. "Thermal performance study of thermochemical reactor using net-packed method," Renewable Energy, Elsevier, vol. 182(C), pages 483-493.

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